Portions of an input measurement sequence are classified into a plurality of regimes by associating each of a plurality of dynamic models with one a switching state such that a model is selected when its associated switching state is true. In a Viterbi-based method, a state transition record is determined, based on the input sequence. A switching state sequence is determined by backtracking through the state transition record. Finally, portions of the input sequence are classified into different regimes, responsive to the switching state sequence. In a variational-based method, the switching state at a particular instance is also determined by a switching model. The dynamic model is then decoupled from the switching model. Parameters of the decoupled dynamic model are determined responsive to a switching state probability estimate. A state of the decoupled dynamic model corresponding to a measurement at the particular instance is estimated, responsive to the input sequence. Parameters of the decoupled switching model are then determined responsive to the dynamic state estimate. A probability is estimated for each possible switching state of the decoupled switching model. A switching state sequence is determined based on the estimated switching state probabilities. Finally, portions of the input sequence are classified into different regimes, responsive to the determined switching state sequence.
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1. A method for classifying portions of an input sequence of measurements into a plurality of regimes, given a set of possible switching states, comprising: associating each of a plurality of dynamic models with a switching state such that a dynamic model is selected when its associated switching state is true, wherein the switching state at a particular instance is determined by a switching model; decoupling the dynamic model from the switching model; determining parameters of the decoupled dynamic model, responsive to a switching state probability estimate; estimating a state of the decoupled dynamic model corresponding to a measurement at the particular instance, and responsive to the input sequence; determining parameters of the decoupled switching model, responsive to the dynamic state estimate; estimating a probability for each possible switching state of the decoupled switching model; determining a switching state sequence based on the estimated switching state probabilities; and classifying portions of the input sequence into different regimes, responsive to the determined switching state sequence.
2. The method of claim 1 wherein classifying is responsive to conditions existing when the input sequence was created.
3. The method of claim 1 wherein regimes are motion regimes.
4. The method of claim 3 , wherein a motion is human motion.
5. The method of claim 4 , wherein human motion comprises at least one of walking, jogging, running, jumping, sitting, and climbing, and ascending and descending a staircase.
6. The method of claim 4 , further comprising: identifying at least one specific individual based on observed dynamics of their motion in image sequences.
7. The method of claim 1 , wherein classifying sequences into motions is used to conduct surveillance.
8. The method of claim 1 , wherein at least one constraint is imposed on classification.
9. The method of claim 1 , wherein sets of dynamic models are used to model qualitatively different regimes of a trajectory with one temporal event.
10. The method of claim 1 , further comprising: performing video compression by transmitting key frames at a low sampling rate, responsive to classifying.
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September 1, 2000
February 17, 2004
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